How Startups Are Using Machine Learning To Improve Customer Service

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In the digital world, personalization is paramount to improve customer experience. Incorporating Machine Learning tools can develop marketing frameworks, elevating the user experience.

Artificial Intelligence and Machine Learning for enterprises and startups dealing in eCommerce – are on the verge of delving into the integration of this software, inevitably ensuring complete automation.

Companies often depend on White Label Local SEO for lead generation to convert the traffic into loyal customers for the growth of their businesses. It is done by focusing on keyword phrases and creating backlinks so that their website ranks high on search engines. The effective use of SEO strategies is done to optimize conversions and consequently, revenues.

It’s useless to generate a traffic stream if it’s not converting into customers. Startups must realize that if they wish to prevent the customer churn, they need to make advances in the way they interact with their visitors, leads, and customers in the first place.

The future of customer engagement can be seen from the graph below. As the tools for Machine Learning improve, interactive channels for customer communication, like chatbots, continue to drive the market.

Challenges In Delivering Customer Satisfaction

Every customer support system consists of a knowledge base or guide that includes appropriate responses to a variety of troubleshooting queries in detail.

Human assistance is limited and can affect the workflow of the customer service support system. Since there is a necessity to record the troubleshooting data into the knowledge base, there is a possibility that varied responses might consist of gibberish and irrelevant references since it won’t be concise, straightforward or summarized to help in later queries.

Customer expectations from an eCommerce business can be seen in the chart above. Ever since Artificial Intelligence (AI) began serving as one of the primary digital solutions for adequately equipped customer experience, the revolution in eCommerce was no longer a revelation. Till today, AI continues to power customer service to generate revenue through the buildup of customer loyalty leading to a significant brand reputation.

How Machine Learning Aims to Improve Customer Service

Customer service has always been a challenge – given the time, efforts, and investment – but with the integration of Machine Learning, interactions with clients, and engagement with prospective customers have become more manageable. According to a mutual consensus by digital marketers, customer service might be a breeze by 2020 since it would take over 85% of communications for a better generation of sales and revenue.

Machine Learning is an attribute of artificial learning that aims to improve user experience by adhering to and churning readily available data. Apart from providing a boost in digital marketing and security, Machine Learning offers adequate support for excellent customer service as well.

To support this, we’ve outlined three ways that Machine Learning can help to improve customer service other than partaking in and securing data collection.

#1 Cost-Effective Configuration

Employing a customer care representative tends to be costly for startups. Since digital media is evolving, businesses seek to hire several customer care representatives that are available around the clock and power all domains of communication.

Hiring a customer care representative might warrant an expenditure ranging from $3,000 – $4,000, which, despite being a one-time expense is comparatively higher than installing an AI computing system. Using chatbots, on the other hand, can save customer care costs up to 30% as they offer an instant response.

Machine Learning helps to improve customer service in such a way that it not only guarantees a one-time permanent solution for businesses that are just starting but also brings a huge asset up front.

For instance, IBM Watson comprises of a highly engaging and interactive AI-powered computing system that aims to get rid of additional and hidden costs of adequate customer care.

Pro Tip: Machine Learning helps humans take care of situations learned through fast and responsive customer care, allowing them to be more proactive and vigilant in their business.

#2 24/7 Omni-channel Support

A report by Gartner predicted that by 2020, most companies would manage 85% of their customer interactions without involving a human. For customer care service centers that operate without appointing representatives in shifts or do not seem to function 24/7 globally, the timings of orders and queries can be subjected to poor and inadequate responsive action.

For instance, your brand’s customer care service timings might not coincide with the schedules that you appear online, which nevertheless, would affect the timely response to any global query.

In such a situation, Machine Learning aims to improve customer service by providing 24/7 support that adds information in a concise yet summarized manner in the knowledge base’s archive. Machine Learning also helps to build the exhaustive knowledge base of any industry, allowing AI to be flexible in its responses for relevant answers to queries.


Consider the Omni-channel customer care desk of, as shown below. Try to integrate your customer care center with an Omni-channel interface so that your chatbot or representative can respond to queries on a single platform altogether. It will also save you the effort of deploying separate teams to hold down the fort across different social media channels and website domains.

Pro Tip: Machine Learning provides exhaustive coverage across web portals, apps, and phone services for prompt and responsive communication.

#3 Personalization


Most companies tend to deploy actual customer care representatives when queries become highly personalized but then interchange with chatbots so that they can adapt to the environment and add to their knowledge base.

This personalization helps customers feel more at ease since they’d prefer talking to a human representative more than catering to a robot on the other side. AI has allowed chatbots to integrate and continually monitor changes into their system so that their technology adapts and keeps wary of human assistance.

Real-time conversations with customers enable a brand to open their can of opportunities – from fashion and lifestyle brands to banks and companies offering services throughout a given range – chatbots can help to ensure conversions without any delay.


LivePerson, an AI-powered company, provides instant messaging technology augmented with Machine Learning for fast and reputed responses. The company reaped $200 million in revenue, mainly due to its maintenance of robust customer interactions.

Automated Email Scanning

According to DigitalGenius, an AI-integrated brand that monitors around 83% of its customer service via Machine Learning, responding to customers within a time frame of 24 hours is the primary goal. Social media fanatics require responses within the hour – especially on Twitter – which if considered; isn’t humanly possible for a single deployed team at hand.

With Machine Learning, systems can now scan emails and tag them according to high relevancy results, as mentioned in their provided knowledge base. It helps the system direct them to the department best suited for monitoring and curating responses for tagged emails.

An AI-enhanced customer care system can also help the most appropriate answers to queries, saving you and your team some time.

Pro Tip: Machine Learning can respond to queries in a streamlined yet automated fashion for the customer’s satisfaction.



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